AI agents are increasingly shaping how people discover, evaluate and choose products. From conversational search to digital assistants and delegated shopping tools, brands now operate in an ecosystem where AI influences recommendations alongside traditional search.
This shift demands a new approach to optimisation; one that is structured, consistent and machine‑readable. But there’s a risk in moving too fast. In the rush to become ‘agent‑friendly’, brands can lose sight of the most important audience of all: the people who ultimately decide to buy.
The agentic era doesn’t replace human experience.
It makes human‑centred design more important than ever.
Optimising for agents and humans: a shared responsibility
Modern SEO is no longer just about technical hygiene or keyword relevance. Emerging LLM audit frameworks show that visibility in AI‑driven systems depends on four interconnected pillars:
- LLM visible – can an agent find, crawl and access your information
- LLM readable – can it correctly interpret your content, structure and context?
- LLM credible – does it recognise your information as trustworthy and authoritative?
- LLM shoppable – is your product data complete, consistent and ready for agent‑driven purchasing?
Together, these pillars form the foundation of agentic visibility. But critically, none of them should come at the expense of usability for people. They should support clearer, more confident human decision‑making, not undermine it.
By bringing together SEO expertise and experimentation thinking, teams can apply these LLM principles alongside real user needs: creating ‘perfect pages’ that work for both machines and humans.
Bringing the LLM pillars to life: reimagining a product page
To understand what this looks like in practice, it helps to ground each pillar in a familiar, real‑world example.
On many fashion product pages, essential details (materials, care instructions, availability or imagery) are hidden behind tabs, toggles or delayed‑loading elements. When information isn’t immediately visible, both people and AI systems can miss critical context.
Why it matters for users
People want fast, friction‑free clarity. When key details are easy to find and consistently presented, shoppers feel more confident and in control, particularly when comparing similar products.
Why it matters for AI
AI agents can’t interpret what they can’t access. Clear, surface‑level visibility helps them understand products more accurately and recommend them more reliably.
LLM Readable: structuring information around real intent
Shoppers rely on attributes such as fit, fabric, care, origin and style notes to make decisions. While this information usually exists, it’s not always structured in a way that reflects how people search, or how AI recognises context.
Why it matters for users
Logical grouping and predictable structure reduce cognitive effort. Customers can scan faster, compare more easily and make decisions with greater confidence.
Why it matters for AI
AI models depend on consistent patterns to map products to intent. Clear, structured attributes help agents place products in the right context, whether that’s sizing, colour, material or styling queries.
LLM Credible: building trust through meaningful signals
Trust isn’t created by product copy alone. Reviews, fit feedback, user‑generated imagery and external validation all play a role in helping people feel confident, and they signal credibility to AI systems too.
Why it matters for users
Seeing how real people experienced a product reduces uncertainty and lowers the emotional risk of buying, particularly in categories like fashion.
Why it matters for AI
AI systems increasingly factor in sentiment, volume and authority. Reviews and user content act as trust signals, helping agents differentiate between similar products and surface those seen as more reliable.
LLM Shoppable: aligning data with real purchase intent
Clear and consistent attributes, from colour names and fits to materials, stock levels and variants, make it easier for both people and AI to understand, compare and select products.
Why it matters for users
Fewer surprises lead to smoother journeys. Clear variant distinctions and reliable availability help shoppers move from consideration to purchase with confidence.
Why it matters for AI
Agents rely on precise, structured data to answer intent‑led queries and power conversational shopping experiences. Clean product information increases the likelihood of accurate matches and relevant recommendations.
Designing for the agentic era isn’t about choosing between machines and people. It’s about recognising that the brands that win will be those that design for both, with clarity, credibility and human needs at the core.
